This notebook is a part of a series that documents the review analysis for the DRN Cell Types project.
This notebook contains the code used to co-cluster the DRN 5-HT neurons in the Zeisel et al. (2018) dataset with our DRN 5-HT neuronal subset. We will exclude the Okaty et al. (2015) cells since there are only 8 cells in that dataset.
library(devtools)
library(useful)
Loading required package: ggplot2
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
library(ggplot2)
library(reticulate)
library(Seurat)
Loading required package: cowplot
Attaching package: ‘cowplot’
The following object is masked from ‘package:ggplot2’:
ggsave
Loading required package: Matrix
library(stringr)
library(Matrix)
library(parallel)
library(ape)
wd <- "/Volumes/LaCie/Dropbox/Sabatini Lab/DRN Cell Types Project/DRN Cell Types Manuscript/Revisions (1)/RNA-seq/"
dRaphe.neurons <- readRDS(file.path(wd, "DRN_inDrop_neurons.rds"))
dRaphe.neurons.5HT <- SubsetData(object = dRaphe.neurons,
ident.use = c("5-HT-I",
"5-HT-II",
"5-HT-III",
"5-HT-IV",
"5-HT-V"),
subset.raw = TRUE)
dim(dRaphe.neurons.5HT@data)
[1] 15941 704
zeisel.ob.SER <- readRDS(file.path(wd, "zeisel_Seurat_HBSER_clustered_filtered.rds"))
dim(zeisel.ob.SER@data)
[1] 13825 374
We will get the cell names from the clustered Zeisel object, but take the count data from the loom file and use the same genes from the inDrop dataset.
Get the cell names:
zeisel.R1DR.cellnames <- WhichCells(object = zeisel.ob.SER,
ident = c("R1 (medial DRN)",
"R1 (lateral DRN)"))
Get the R1DR cells from the count matrix:
library(loomR)
Loading required package: R6
Loading required package: hdf5r
Attaching package: ‘loomR’
The following object is masked from ‘package:dplyr’:
combine
The following object is masked from ‘package:devtools’:
create
lfile <- connect(filename = "/Volumes/LaCie/Dropbox/Sabatini Lab/DRN Cell Types Project/DRN Cell Types Manuscript/Revisions (1)/RNA-seq/l6_r3_cholinergic_monoaminergic_and_peptidergic_neurons.loom")
zeisel.expr <- lfile$matrix[,]
zeisel.expr <- t(zeisel.expr)
zeisel.cellIDs <- lfile$col.attrs$CellID[]
zeisel.genes <- lfile$row.attrs$Gene[]
zeisel.genes.fix <- str_replace(string = zeisel.genes,
pattern = "-",
replacement = ".")
zeisel.expr <- as.data.frame(x = zeisel.expr,
row.names = zeisel.genes.fix)
colnames(zeisel.expr) <- zeisel.cellIDs
lfile$close_all()
zeisel.expr.R1DR <- zeisel.expr[,zeisel.R1DR.cellnames]
dim(zeisel.expr.R1DR)
[1] 27998 118
We will only use cells in each dataset that are from the DRN and exclude 5-HT neurons from other structures/rhombomeres for this analysis. This should allow us to see if subtypes that we have identified in our dataset are also present in these other datasets.
huang.genes <- rownames(dRaphe.neurons.5HT@data)
sum(is.element(huang.genes, zeisel.genes.fix))
[1] 13931
head(huang.genes[!is.element(huang.genes, zeisel.genes.fix)],100)
[1] "X.343C11.2" "X0610007P14Rik" "X0610009B22Rik" "X0610009E02Rik" "X0610009L18Rik"
[6] "X0610009O20Rik" "X0610010F05Rik" "X0610012G03Rik" "X0610030E20Rik" "X0610031O16Rik"
[11] "X0610037L13Rik" "X0610040B10Rik" "X0610040F04Rik" "X0610040J01Rik" "X0610043K17Rik"
[16] "X1110002E22Rik" "X1110002L01Rik" "X1110003F10Rik" "X1110004E09Rik" "X1110004F10Rik"
[21] "X1110008F13Rik" "X1110008L16Rik" "X1110008P14Rik" "X1110015O18Rik" "X1110017D15Rik"
[26] "X1110019D14Rik" "X1110025M09Rik" "X1110032A03Rik" "X1110037F02Rik" "X1110038B12Rik"
[31] "X1110038F14Rik" "X1110051M20Rik" "X1110059E24Rik" "X1110059G10Rik" "X1110065P20Rik"
[36] "X1190002N15Rik" "X1190007I07Rik" "X1300002E11Rik" "X1300017J02Rik" "X1500002C15Rik"
[41] "X1500004A13Rik" "X1500005C15Rik" "X1500009C09Rik" "X1500009L16Rik" "X1500011B03Rik"
[46] "X1500011K16Rik" "X1500015A07Rik" "X1500015O10Rik" "X1500017E21Rik" "X1500026H17Rik"
[51] "X1600002H07Rik" "X1600002K03Rik" "X1600012H06Rik" "X1600014C10Rik" "X1600020E01Rik"
[56] "X1600023N17Rik" "X1600029O15Rik" "X1700001L19Rik" "X1700001O22Rik" "X1700003E16Rik"
[61] "X1700003M07Rik" "X1700006J14Rik" "X1700007K13Rik" "X1700008J07Rik" "X1700010I14Rik"
[66] "X1700012B09Rik" "X1700012D14Rik" "X1700016K19Rik" "X1700017B05Rik" "X1700017D01Rik"
[71] "X1700019B21Rik" "X1700019D03Rik" "X1700020I14Rik" "X1700021A07Rik" "X1700021F05Rik"
[76] "X1700025G04Rik" "X1700028E10Rik" "X1700028J19Rik" "X1700028K03Rik" "X1700029J07Rik"
[81] "X1700030C10Rik" "X1700030K09Rik" "X1700031P21Rik" "X1700037C18Rik" "X1700037H04Rik"
[86] "X1700040L02Rik" "X1700041G16Rik" "X1700042O10Rik" "X1700047I17Rik2" "X1700047M11Rik"
[91] "X1700052K11Rik" "X1700055D18Rik" "X1700056N10Rik" "X1700063D05Rik" "X1700066B19Rik"
[96] "X1700066M21Rik" "X1700073E17Rik" "X1700086L19Rik" "X1700086O06Rik" "X1700086P04Rik"
tail(huang.genes[!is.element(huang.genes, zeisel.genes.fix)],100)
[1] "Gm42568" "Gm42633" "Gm42648" "Gm42668" "Gm42689" "Gm42718"
[7] "Gm42770" "Gm42819" "Gm42853" "Gm42860" "Gm42969" "Gm43103"
[13] "Gm43149" "Gm43180" "Gm43205" "Gm43210" "Gm43237" "Gm43268"
[19] "Gm43279" "Gm43336" "Gm43457" "Gm43490" "Gm43547" "Gm43677"
[25] "Gm43776" "Gm43795" "Gm43879" "Gm43884" "Gm43920" "Gm44033"
[31] "Gm44041" "Gm44044" "Gm44229" "Gm44438" "Gm44439" "Gm44440"
[37] "Gm44542" "Gm44630" "Gm44682" "Gm44797" "Gm44799" "Gm44834"
[43] "Gm44848" "Gm44860" "Gm44878" "Gm44913" "Gm45019" "Gm45069"
[49] "Gm45211" "Gm45251" "Gm45287" "Gm45319" "Gm45407" "Gm45416"
[55] "Gm45605" "Gm45652" "Gm4604" "Gm4651" "Gm5297" "Gm5586"
[61] "Gm5678" "Gm5776" "Gm6028" "Gm6394" "Gm6395" "Gm6498"
[67] "Gm6905" "Gm7123" "Gm7571" "Gm8463" "Ifi213" "Lipo3"
[73] "Mfsd13a" "Mrln" "N4bp2os" "Nectin2" "Platr9" "Prmt9"
[79] "RP23.114G13.1" "RP23.137G18.2" "RP23.177D16.3" "RP23.177D16.5" "RP23.181A8.2" "RP23.240G3.2"
[85] "RP23.257B9.1" "RP23.269H21.1" "RP23.314A15.5" "RP23.451B4.1" "RP23.451H9.2" "RP23.69L13.2"
[91] "RP24.189I2.4" "RP24.362N13.1" "RP24.369J8.1" "RP24.418N13.2" "RP24.571A14.6" "Rpap3.ps2"
[97] "Rpl10.ps2" "Rpl30.ps11" "Txn.ps1" "Zfp975"
Use the intersection:
genes.use <- huang.genes[is.element(huang.genes, zeisel.genes.fix)]
length(genes.use)
[1] 13931
Subset genes for both datasets:
zeisel.expr.R1DR <- zeisel.expr.R1DR[genes.use,]
dim(zeisel.expr.R1DR)
[1] 13931 118
huang.expr <- as.matrix(dRaphe.neurons.5HT@raw.data)
huang.expr <- huang.expr[genes.use,]
dim(huang.expr)
[1] 13931 704
huang.metadata <- dRaphe.neurons.5HT@meta.data[,c("BatchID", "Sex", "subtypeIDs")]
colnames(huang.metadata) <- c("BatchID", "Sex", "orig.clusterNames")
huang.metadata$BatchID <- as.factor(huang.metadata$BatchID)
zeisel.metadata <- as.data.frame(matrix(data = NA,
nrow = length(zeisel.R1DR.cellnames),
ncol = 2),
row.names = zeisel.R1DR.cellnames)
zeisel.metadata <- cbind(zeisel.metadata,
zeisel.ob.SER@meta.data[zeisel.R1DR.cellnames, c("zeisel.clusterNames")])
colnames(zeisel.metadata) <- c("BatchID", "Sex", "orig.clusterNames")
zeisel.metadata$BatchID <- as.factor(zeisel.metadata$BatchID)
zeisel.metadata$Sex <- as.factor(zeisel.metadata$Sex)
huang.ob <- CreateSeuratObject(raw.data = huang.expr,
meta.data = huang.metadata,
project = "Huang_inDrops",
min.cells = 0,
min.genes = 0,
scale.factor = 10000)
zeisel.ob <- CreateSeuratObject(raw.data = zeisel.expr.R1DR,
meta.data = zeisel.metadata,
project = "Zeisel_10X",
min.cells = 0,
min.genes = 0,
scale.factor = 10000)
mito.genes <- grep("^mt.", rownames(huang.ob@data), value = TRUE)
percent.mito <- Matrix::colSums(huang.ob@data[mito.genes, ])/Matrix::colSums(huang.ob@data)
huang.ob <- AddMetaData(huang.ob, percent.mito, "percent.mito")
percent.mito <- Matrix::colSums(zeisel.ob@data[mito.genes, ])/Matrix::colSums(zeisel.ob@data)
zeisel.ob <- AddMetaData(zeisel.ob, percent.mito, "percent.mito")
rm(percent.mito)
huang.ob <- NormalizeData(object = huang.ob,
normalization.method = "LogNormalize",
scale.factor = 10000,
display.progress = FALSE)
zeisel.ob <- NormalizeData(object = zeisel.ob,
normalization.method = "LogNormalize",
scale.factor = 10000,
display.progress = FALSE)
huang.ob <- ScaleData(object = huang.ob,
vars.to.regress = c("nUMI", "percent.mito", "BatchID"),
model.use = "linear",
do.scale = TRUE,
scale.max = 10,
do.center = TRUE,
do.par = TRUE,
num.cores = 4,
display.progress = FALSE)
zeisel.ob <- ScaleData(object = zeisel.ob,
vars.to.regress = c("nUMI", "percent.mito"),
model.use = "linear",
do.scale = TRUE,
scale.max = 10,
do.center = TRUE,
do.par = TRUE,
num.cores = 4,
display.progress = FALSE)
huang.ob <- FindVariableGenes(object = huang.ob,
mean.function = ExpMean,
dispersion.function = LogVMR,
x.low.cutoff = 0.075,
x.high.cutoff = 4,
y.cutoff = 0.5,
num.bin = 100)
Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
length(huang.ob@var.genes)
[1] 2366
zeisel.ob <- FindVariableGenes(object = zeisel.ob,
mean.function = ExpMean,
dispersion.function = LogVMR,
x.low.cutoff = 0.075,
x.high.cutoff = 4,
y.cutoff = 0.5,
num.bin = 100)
Calculating gene means
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
length(zeisel.ob@var.genes)
[1] 1990
hvgs <- union(huang.ob@var.genes, zeisel.ob@var.genes)
length(hvgs)
[1] 3920
huang.ob@meta.data$dataset <- "Huang"
zeisel.ob@meta.data$dataset <- "Zeisel"
combined.ob <- RunCCA(object = huang.ob,
object2 = zeisel.ob,
genes.use = hvgs,
num.cc = 30,
scale.data = TRUE)
Running CCA
Merging objects
Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Scaling data matrix
|
| | 0%
|
|==============================================================================================| 100%
for (i in 1:10) {
DimHeatmap(object = combined.ob,
reduction.type = "cca",
cells.use = 200,
dim.use = (3*(i-1)+1):(3*i),
do.balanced = TRUE)
}
MetageneBicorPlot(object = combined.ob,
grouping.var = "dataset",
dims.eval = 1:30,
display.progress = FALSE)
Rescaling group 1
Rescaling group 2
combined.ob <- AlignSubspace(object = combined.ob,
reduction.type = "cca",
grouping.var = "dataset",
dims.align = 1:7,
verbose = FALSE)
VlnPlot(object = combined.ob,
features.plot = c("CC1", "ACC1", "CC2", "ACC2",
"CC3", "ACC3", "CC4", "ACC4"),
nCol = 4,
group.by = "dataset",
point.size.use = 0)
combined.ob <- RunUMAP(object = combined.ob,
reduction.use = "cca.aligned",
dims.use = 1:7,
n_neighbors = 30L,
min_dist = 0.1,
metric = "correlation")
Re-label cells by dataset + cluster names:
huang.cellnames.I <- WhichCells(object = dRaphe.neurons.5HT,
ident = "5-HT-I")
huang.cellnames.II <- WhichCells(object = dRaphe.neurons.5HT,
ident = "5-HT-II")
huang.cellnames.III <- WhichCells(object = dRaphe.neurons.5HT,
ident = "5-HT-III")
huang.cellnames.IV <- WhichCells(object = dRaphe.neurons.5HT,
ident = "5-HT-IV")
huang.cellnames.V <- WhichCells(object = dRaphe.neurons.5HT,
ident = "5-HT-V")
zeisel.cellnames.l <- WhichCells(object = zeisel.ob.SER,
ident = "R1 (lateral DRN)")
zeisel.cellnames.m <- WhichCells(object = zeisel.ob.SER,
ident = "R1 (medial DRN)")
combined.ob <- SetIdent(object = combined.ob,
cells.use = huang.cellnames.I,
ident.use = "Huang_5-HT-I")
combined.ob <- SetIdent(object = combined.ob,
cells.use = huang.cellnames.II,
ident.use = "Huang_5-HT-II")
combined.ob <- SetIdent(object = combined.ob,
cells.use = huang.cellnames.III,
ident.use = "Huang_5-HT-III")
combined.ob <- SetIdent(object = combined.ob,
cells.use = huang.cellnames.IV,
ident.use = "Huang_5-HT-IV")
combined.ob <- SetIdent(object = combined.ob,
cells.use = huang.cellnames.V,
ident.use = "Huang_5-HT-V")
combined.ob <- SetIdent(object = combined.ob,
cells.use = zeisel.cellnames.l,
ident.use = "Zeisel_R1DR-lateral")
combined.ob <- SetIdent(object = combined.ob,
cells.use = zeisel.cellnames.m,
ident.use = "Zeisel_R1DR-medial")
p1 <- DimPlot(object = combined.ob,
reduction.use = "umap",
do.label = FALSE,
no.legend = FALSE,
pt.size = 3,
group.by = "dataset",
do.return = TRUE)
p2 <- DimPlot(object = combined.ob,
reduction.use = "umap",
do.label = FALSE,
no.legend = FALSE,
pt.size = 3,
do.return = TRUE)
plot_grid(p1, p2)
FeaturePlot(object = combined.ob,
nCol = 4,
reduction.use = "umap",
features.plot = c("Slc6a4", "Tph2", "Fev", "En1",
"Prkcq", "Asb4", "Hcrtr1", "Trh",
"Pdyn", "Slc17a8", "Cbln2", "Met"),
cols.use = c("gray", "red"),
no.legend = FALSE,
pt.size = 1)
combined.ob <- RunPCA(object = combined.ob,
pc.genes = hvgs,
pcs.compute = 30,
weight.by.var = FALSE,
pcs.print = NA)
for (i in 1:10) {
DimHeatmap(object = combined.ob,
reduction.type = "pca",
cells.use = 200,
dim.use = (3*(i-1)+1):(3*i),
do.balanced = TRUE)
}
PCElbowPlot(object = combined.ob,
num.pc = 30)
combined.ob <- RunUMAP(object = combined.ob,
reduction.use = "pca",
dims.use = 1:11,
n_neighbors = 30L,
min_dist = 0.1,
metric = "correlation")
p3 <- DimPlot(object = combined.ob,
reduction.use = "umap",
do.label = FALSE,
no.legend = FALSE,
pt.size = 3,
group.by = "dataset",
do.return = TRUE)
p4 <- DimPlot(object = combined.ob,
reduction.use = "umap",
do.label = FALSE,
no.legend = FALSE,
pt.size = 3,
do.return = TRUE)
plot_grid(p3, p4)
p3b <- DimPlot(object = combined.ob,
reduction.use = "umap",
do.label = FALSE,
no.legend = FALSE,
pt.size = 3,
group.by = "BatchID",
do.return = TRUE)
plot_grid(p3, p3b)
FeaturePlot(object = combined.ob,
nCol = 4,
reduction.use = "umap",
features.plot = c("Slc6a4", "Tph2", "Fev", "En1",
"Prkcq", "Asb4", "Hcrtr1", "Trh",
"Pdyn", "Slc17a8", "Cbln2", "Met"),
cols.use = c("gray", "red"),
no.legend = FALSE,
pt.size = 1)
FeaturePlot(object = combined.ob,
nCol = 4,
reduction.use = "umap",
features.plot = c("nUMI", "nGene", "percent.mito", "Snap25",
"PC1", "PC2", "PC3", "PC4",
"PC5", "PC6", "PC7", "PC8"),
cols.use = c("gray", "red"),
no.legend = FALSE,
pt.size = 1)
Try running UMAP but now excluding PCs 1:3 – likely related to batch effects separating datasets and batches.
combined.ob <- RunUMAP(object = combined.ob,
reduction.use = "pca",
dims.use = 4:10,
n_neighbors = 30L,
min_dist = 0.1,
metric = "correlation")
p5 <- DimPlot(object = combined.ob,
reduction.use = "umap",
do.label = FALSE,
no.legend = FALSE,
pt.size = 3,
group.by = "dataset",
do.return = TRUE)
p6 <- DimPlot(object = combined.ob,
reduction.use = "umap",
do.label = FALSE,
no.legend = FALSE,
pt.size = 3,
do.return = TRUE)
plot_grid(p5, p6)
plot_grid(p4, p6)
plot_grid(p2, p6)
FeaturePlot(object = combined.ob,
nCol = 4,
reduction.use = "umap",
features.plot = c("Slc6a4", "Tph2", "Fev", "En1",
"Prkcq", "Asb4", "Hcrtr1", "Trh",
"Pdyn", "Slc17a8", "Cbln2", "Met"),
cols.use = c("gray", "red"),
no.legend = FALSE,
pt.size = 1)
Zeisel R1DR neurons are appearing in the different subtype clusters from our dataset, rather than separating out.
saveRDS(object = combined.ob,
file = file.path(wd, "Combined_Huang_5-HT_Zeisel_R1DR.rds"))
Machine specifications:
devtools::session_info()
Session info ------------------------------------------------------------------------------------------
setting value
version R version 3.4.4 (2018-03-15)
system x86_64, darwin15.6.0
ui RStudio (1.1.447)
language (EN)
collate en_US.UTF-8
tz America/New_York
date 2019-05-23
Packages ----------------------------------------------------------------------------------------------
package * version date source
abind 1.4-5 2016-07-21 CRAN (R 3.4.0)
acepack 1.4.1 2016-10-29 cran (@1.4.1)
ape * 5.1 2018-04-04 CRAN (R 3.4.4)
assertthat 0.2.0 2017-04-11 CRAN (R 3.4.0)
backports 1.1.2 2017-12-13 CRAN (R 3.4.3)
base * 3.4.4 2018-03-15 local
base64enc 0.1-3 2015-07-28 cran (@0.1-3)
bindr 0.1.1 2018-03-13 CRAN (R 3.4.4)
bindrcpp * 0.2.2 2018-03-29 CRAN (R 3.4.4)
bit 1.1-14 2018-05-29 CRAN (R 3.4.4)
bit64 0.9-7 2017-05-08 CRAN (R 3.4.0)
bitops 1.0-6 2013-08-17 cran (@1.0-6)
broom 0.4.4 2018-03-29 CRAN (R 3.4.4)
caret 6.0-80 2018-05-26 CRAN (R 3.4.4)
caTools 1.17.1 2014-09-10 cran (@1.17.1)
checkmate 1.8.5 2017-10-24 CRAN (R 3.4.2)
class 7.3-14 2015-08-30 CRAN (R 3.4.4)
cluster 2.0.7-1 2018-04-09 CRAN (R 3.4.4)
codetools 0.2-15 2016-10-05 CRAN (R 3.4.4)
colorspace 1.3-2 2016-12-14 CRAN (R 3.4.0)
compiler 3.4.4 2018-03-15 local
cowplot * 0.9.2 2017-12-17 CRAN (R 3.4.3)
CVST 0.2-2 2018-05-26 CRAN (R 3.4.4)
data.table 1.10.4-3 2017-10-27 CRAN (R 3.4.2)
datasets * 3.4.4 2018-03-15 local
ddalpha 1.3.2 2018-04-08 CRAN (R 3.4.4)
DEoptimR 1.0-8 2016-11-19 cran (@1.0-8)
devtools * 1.13.5 2018-02-18 CRAN (R 3.4.3)
diffusionMap 1.1-0 2014-02-20 cran (@1.1-0)
digest 0.6.15 2018-01-28 CRAN (R 3.4.3)
dimRed 0.1.0 2017-05-04 CRAN (R 3.4.0)
diptest 0.75-7 2016-12-05 cran (@0.75-7)
doSNOW 1.0.16 2017-12-13 CRAN (R 3.4.3)
dplyr * 0.7.5 2018-05-19 CRAN (R 3.4.4)
DRR 0.0.3 2018-01-06 CRAN (R 3.4.3)
dtw 1.20-1 2018-05-18 CRAN (R 3.4.4)
evaluate 0.10.1 2017-06-24 cran (@0.10.1)
fitdistrplus 1.0-9 2017-03-24 CRAN (R 3.4.0)
flexmix 2.3-14 2017-04-28 cran (@2.3-14)
FNN 1.1 2013-07-31 cran (@1.1)
foreach 1.4.4 2017-12-12 CRAN (R 3.4.3)
foreign 0.8-70 2018-04-23 CRAN (R 3.4.4)
Formula 1.2-3 2018-05-03 CRAN (R 3.4.4)
fpc 2.1-11 2018-01-13 CRAN (R 3.4.3)
gdata 2.18.0 2017-06-06 cran (@2.18.0)
geometry 0.3-6 2015-09-09 CRAN (R 3.4.0)
ggplot2 * 2.2.1 2016-12-30 CRAN (R 3.4.0)
ggridges 0.5.0 2018-04-05 CRAN (R 3.4.4)
glue 1.3.1 2019-03-12 cran (@1.3.1)
gower 0.1.2 2017-02-23 CRAN (R 3.4.0)
gplots 3.0.1 2016-03-30 cran (@3.0.1)
graphics * 3.4.4 2018-03-15 local
grDevices * 3.4.4 2018-03-15 local
grid 3.4.4 2018-03-15 local
gridExtra 2.3 2017-09-09 CRAN (R 3.4.1)
gtable 0.2.0 2016-02-26 CRAN (R 3.4.0)
gtools 3.5.0 2015-05-29 cran (@3.5.0)
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Hmisc 4.1-1 2018-01-03 CRAN (R 3.4.3)
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htmltools 0.3.6 2017-04-28 cran (@0.3.6)
htmlwidgets 1.2 2018-04-19 CRAN (R 3.4.4)
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knitr 1.20 2018-02-20 CRAN (R 3.4.3)
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lattice 0.20-35 2017-03-25 CRAN (R 3.4.4)
latticeExtra 0.6-28 2016-02-09 cran (@0.6-28)
lava 1.6.1 2018-03-28 CRAN (R 3.4.4)
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mixtools 1.1.0 2017-03-10 cran (@1.1.0)
mnormt 1.5-5 2016-10-15 cran (@1.5-5)
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stringi 1.4.3 2019-03-12 cran (@1.4.3)
stringr * 1.4.0 2019-02-10 cran (@1.4.0)
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tclust 1.4-1 2018-05-24 CRAN (R 3.4.4)
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